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1401312

全球情感运算市场 - 2023-2030

Global Affective Computing Market - 2023-2030

出版日期: 2023年12月29日 | 出版商:

DataM Intelligence | 英文 211 Pages | 商品交期: 最快1-2个工作天内

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简介目录

概述

全球情绪运算市场在2022年达到509亿美元，预计2030年将达到5,929亿美元，2023-2030年预测期间CAGR为36.2%。

深度学习中人工智慧和机器学习技术的进步显着增强了情绪运算系统的能力。先进的演算法现在可以更准确地分析和解释复杂的情绪线索。对自然、直觉的人机互动的需求不断增长，正在推动情感运算的采用。企业和产业正在利用机器中的情感智慧来增强使用者体验和参与度。

穿戴式装置的激增和物联网的扩展为整合情感运算提供了机会。配备情绪识别感测器的可穿戴设备和具有情绪感知功能的物联网设备有助于市场成长。虚拟助理和聊天机器人在从客户服务到虚拟伴侣的各种应用中的广泛使用，正在推动对有效计算的需求。情绪智商虚拟助理可增强使用者互动和满意度。

由于情感运算在个人化学习教育中的使用越来越多，北美成为全球情感运算市场的主导地区。该地区对创新和研究驱动型发展的重视有助于情感识别、情感分析和情感计算应用相关技术的进步。北美的医疗保健、零售和娱乐等行业很早就表现出了对情感运算应用程式的兴趣和采用。

动力学

对虚拟助理的需求不断增长

情感运算允许虚拟助理根据使用者的情绪表达来个性化他们的反应。个人化程度有助于打造更量身订做、更具吸引力的使用者体验。情感运算技术使虚拟助理能够成为具有情感智慧的会话代理。它识别并回应使用者的情绪，创造更自然、更富同理心的互动。由情感运算提供支援的虚拟助理可以根据使用者的情绪状态调整其介面和回应。这种适应性有助于提供动态且以使用者为中心的体验。

在客户服务应用中，配备情绪运算功能的虚拟助理可以更好地理解和处理客户的情绪和情绪。这对于解决问题和提供支援特别有价值。情感计算有助于识别使用者声音中的情感。无论是智慧型手机、智慧型扬声器或其他装置中的虚拟助理都使用此功能根据侦测到的情绪基调自订回应和互动。

技术进步

机器学习和人工智慧的不断进步有助于开发更复杂的情感识别演算法。改进的演算法提高了情绪计算系统的准确性和效率。脸部辨识摄影机、语音辨识麦克风和生理感测器等感测器技术的进步有助于更好的资料撷取和分析。增强的感测技术可以更精确地测量情绪线索。

深度学习和神经网路的发展带来了模式识别的突破，使情绪运算系统能够识别脸部表情、语音和其他情绪讯号中的复杂模式。技术进步使得情绪辨识的多种模式得以集成，例如将脸部表情与语音分析和生理讯号结合。多模态方法提高了情绪分析的全面性。

精度和可靠性低

情感计算系统严重依赖准确识别和解释人类情感的演算法。情绪辨识准确度低会导致对使用者情绪状态的误解，影响技术的可靠性。对情绪线索的解释是主观的并且依赖上下文。情感计算演算法很难一致地解释不同个体和情况下的不同情感表达，从而导致结果不一致。

人类的情绪很复杂，表现形式多种多样，因此开发准确涵盖所有情绪状态的演算法具有挑战性。表达式中的细微差别和变化增加了复杂性。不同文化的情感表达方式不同，情感计算系统并不总是能解释这些文化差异。这会导致对情绪线索的误解，尤其是在多元化和全球性的使用者群体中。

Technological advancements in AI and ML technologies in deep learning significantly enhanced the capabilities of affective computing systems. Advanced algorithms now analyze and interpret complex emotional cues with greater accuracy. The rising demand for natural and intuitive human-machine interaction is driving the adoption of affective computing. Businesses and industries are leveraging emotional intelligence in machines to enhance user experiences and engagement.

The proliferation of wearable devices and the expansion of the Internet of Things provide opportunities for integrating affective computing. Wearables equipped with sensors for emotion recognition and IoT devices with emotion-aware features contribute to market growth. The widespread use of virtual assistants and chatbots in various applications, from customer service to virtual companions, is fueling the demand for effective computing. Emotionally intelligent virtual assistants enhance user interactions and satisfaction.

North America is a dominating region in the global affective computing market due to the growing use of affective computing in education for personalized learning. The region's emphasis on innovation and research-driven development contributes to the advancement of technologies related to emotion recognition, sentiment analysis and affective computing applications. Industries such as healthcare, retail and entertainment in North America have shown early interest and adoption of affective computing applications.

Dynamics

Growing Demand for Virtual Assistants

Affective computing allows virtual assistants to personalize their responses based on users' emotional expressions. The level of personalization contributes to a more tailored and engaging user experience. Affective computing technologies enable virtual assistants to become emotionally intelligent conversational agents. It recognize and respond to users' emotions, creating a more natural and empathetic interaction. Virtual assistants, powered by affective computing, adapt their interfaces and responses based on users' emotional states. The adaptability contributes to a dynamic and user-centric experience.

In customer service applications, virtual assistants equipped with affective computing capabilities better understand and address customers' emotions and sentiments. The is particularly valuable for resolving issues and providing support. Affective computing facilitates the recognition of emotions in users' voices. Virtual assistants, whether in smartphones, smart speakers or other devices use this capability to tailor responses and interactions based on the detected emotional tone.

Technological Advancement

Ongoing advancements in machine learning and artificial intelligence contribute to the development of more sophisticated algorithms for emotion recognition. Improved algorithms enhance the accuracy and efficiency of affective computing systems. Progress in sensor technologies, including facial recognition cameras, voice recognition microphones and physiological sensors, contributes to better data capture and analysis. Enhanced sensing technologies enable more precise measurement of emotional cues.

The evolution of deep learning and neural networks has led to breakthroughs in pattern recognition, enabling affective computing systems to discern intricate patterns in facial expressions, voice Tons and other emotional signals. Technological advancements enable the integration of multiple modalities for emotion recognition, such as combining facial expressions with voice analysis and physiological signals. The multi-modal approach improves the comprehensiveness of emotional analysis.

Low Accuracy and Reliability

Affective computing systems heavily rely on algorithms designed to recognize and interpret human emotions accurately. Low accuracy in emotion recognition lead to misinterpretation of users' emotional states, affecting the reliability of the technology. The interpretation of emotional cues is subjective and context-dependent. Affective computing algorithms struggle to consistently interpret diverse emotional expressions across different individuals and situations, leading to inconsistencies in results.

Human emotions are complex and manifest in a wide range of expressions, making it challenging to develop algorithms that cover the full spectrum of emotional states accurately. Subtle nuances and variations in expressions add to the complexity. Emotions are expressed differently across cultures and affective computing systems do not always account for these cultural variations. The results in misinterpretations of emotional cues, especially in diverse and global user populations.

Segment Analysis

The global affective computing market is segmented based on technology, component, enterprise size, end-user and region.

Growing Adoption of Touch-based Technology in Affective Computing Market

Based on the technology, the affective computing market is segmented into touch-based and touchless. Touch-based technology is a more natural form of human-computer interaction compared to touchless technology. Touch-based sensors and devices capture subtle nuances in touch interactions, providing a means to recognize and interpret emotional cues. The pressure, duration and patterns of touch convey emotional information, contributing to affective computing applications.

The widespread adoption of smartphones, tablets and wearables has driven the integration of touch-based interfaces. The devices often incorporate touch sensors to facilitate user interactions. The use of affective computing in these devices enhances user experiences, especially in applications related to health and wellness.

Haptic feedback, a component of touch-based technology, allows devices to provide tactile sensations in response to user interactions. The feature enhances emotional engagement by creating a sense of touch, adding an extra dimension to the user experience. Growing product launches in the automotive industry with touch-based affective computing help to boost segment growth over the forecast period.

For instance, on August 15, 2022, Mahindra & Mahindra, India's leading SUV manufacturer launched its new state-of-the-art INGLO EV platform and five e-SUVs under two EV brands showcasing its vision for the future of electric mobility. The brake-by-wire technology is completely decoupled from the hydraulic system; this allows multiple brake modes for pedal feel and recuperation. Its behind the wheel enjoy the Intelligent Drive Modes that govern various aspects including modulation of powertrain response, suspension response, brake feel, electronic stability control intervention and many more features at the touch of a button

Geographical Penetration

North America is a Dominating Affective Computing Market Due To The Rapid Growth In Research

North America accounted for the largest market share in the global affective computing market due to the growing research and innovation in the region. North America is renowned for leading advances in technical innovation. A robust ecosystem of startups, research centers and technology firms exist in the area, all of which actively support the creation and application of efficient computer technologies. Affective computing is an area of study that is heavily researched by renowned research institutions and universities in North America.

Growing technological advancements in the region help to boost the regional market growth. For instance, on August 03, 2022, Gartner identified four emerging technologies expected to have a transformational impact on digital advertising. The four technologies are artificial intelligence (AI) for marketing, emotion AI, influence engineering and generative AI. A technology or application's evolutionary trajectory might be seen through the Gartner Hype Cycle, which offers valuable insights for managing the implementation of a particular business objective.

Competitive Landscape

The major global players in the market include Amazon Web Services Inc., Affectiva Inc., Nuance Communications Inc., Nemesysco Ltd., Eyesight Technologies Ltd., Element Human Ltd., Emotibot Technologies Limited, Kairos AR, Inc., Realeyes Data Services Ltd. and AUDEERING GmbH.

COVID-19 Impact Analysis

The pandemic accelerated the pace of digital transformation across industries as organizations sought to adapt to remote work, virtual communication and changes in consumer behavior. Affective Computing technologies, which focus on understanding and responding to human emotions have found increased relevance in virtual communication tools and customer engagement platforms.

Affective Computing plays a role in healthcare applications, including mental health monitoring and virtual care. With the increased demand for remote healthcare solutions during the pandemic, there could be a growing interest in technologies that facilitate emotional understanding and well-being monitoring.

Remote work and the challenges associated with it, including isolation and stress, prompted organizations to focus on employee well-being. Affective Computing tools that gauge and respond to employee emotions have gained attention in the context of remote workforce management. With changes in consumer behavior and an increased reliance on online services, businesses have looked to affective computing solutions to enhance virtual customer interactions. Understanding customer emotions and preferences becomes crucial in a digital-first environment.

Russia-Ukraine War Impact Analysis

Conflict disrupts supply chains, it impacts the availability of components and materials needed for the production of technology products, including affective computing solutions. Geopolitical tensions contribute to economic uncertainties, affecting business and consumer confidence. The influences investment decisions and purchasing behaviors, potentially impacting the adoption of affective computing technologies.

Governments introduce new regulations or change existing ones in response to geopolitical events. The regulatory changes affect the operations and market conditions for technology companies, including those in the affective computing sector. Geopolitical events influence global market sentiment. Investors respond to uncertainties by adjusting their portfolios, which have broader implications for technology stocks and investments.

The affective computing market, like technology markets, often involves international collaboration and partnerships. Geopolitical tensions affect such collaborations, leading to changes in research and development initiatives. Uncertain geopolitical situations influence consumer behavior. Changes in consumer confidence and spending patterns impact the market demand for affective computing applications, especially in sectors such as retail, entertainment and customer service.

By Technology

Touch-based
Touchless

By Component

Software
- Speech Recognition
- Gesture Recognition
- Facial Feature Extraction
- Analytics Software
- Enterprise Software
Hardware
- Sensors
- Cameras
- Storage Devices and Processors
- Others

By Enterprise Size

Small and Medium Enterprises
Large Enterprises

By End-User

Academia and Research
Media and Entertainment
Government and Defense
Healthcare and Life Sciences
IT and Telecom
Retail and E-Commerce
Automotive
BFSI
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

On May 05, 2021, Affectiva acquired Smart Eye, the global leader in eye tracking and driver monitoring systems. By merging their highly skilled teams and industry-leading technologies they bring to market unmatched AI solutions for the automotive industry and media analytics.
On February 23, 2021, IBM announced the deployment of "PROPEL-i," a customized end-to-end cloud-native logistics platform created in partnership with IBM Global Business Services, by Safe Xpress, the top supply chain and logistics firm in India.
On May 25, 2023, to help clients select investments, JPMorgan created a ChatGPT-like software program that uses a cutting-edge kind of artificial intelligence. The corporation applied to trademark a product named IndexGPT, as per a document from the bank located in New York.

Why Purchase the Report?

To visualize the global affective computing market segmentation based on technology, component, enterprise size, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of affective computing market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global affective computing market report would provide approximately 69 tables, 70 figures and 211 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Technology
3.2. Snippet by Component
3.3. Snippet by Enterprise Size
3.4. Snippet by End-User
3.5. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Growing Demand for Virtual Assistants
  - 4.1.1.2. Technological Advancement
- 4.1.2. Restraints
  - 4.1.2.1. Low Accuracy and Reliability
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Technology

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 7.1.2. Market Attractiveness Index, By Technology
7.2. Touch-based*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Touchless

8. By Component

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 8.1.2. Market Attractiveness Index, By Component
8.2. Software*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  - 8.2.2.1. Speech Recognition
  - 8.2.2.2. Gesture Recognition
  - 8.2.2.3. Facial Feature Extraction
  - 8.2.2.4. Analytics Software
  - 8.2.2.5. Enterprise Software
8.3. Hardware
- 8.3.1. Sensors
- 8.3.2. Cameras
- 8.3.3. Storage Devices and Processors
- 8.3.4. Others

9. By Enterprise Size

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 9.1.2. Market Attractiveness Index, By Enterprise Size
9.2. Small and Medium Enterprises*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Large Enterprises

10. By End-User

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.1.2. Market Attractiveness Index, By End-User
10.2. Academia and Research*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Media and Entertainment
10.4. Government and Defense
10.5. Healthcare and Life Sciences
10.6. IT and Telecom
10.7. Retail and E-Commerce
10.8. Automotive
10.9. BFSI
10.10. Others

11. By Region

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 11.1.2. Market Attractiveness Index, By Region
11.2. North America
- 11.2.1. Introduction
- 11.2.2. Key Region-Specific Dynamics
- 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.2.7.1. U.S.
  - 11.2.7.2. Canada
  - 11.2.7.3. Mexico
11.3. Europe
- 11.3.1. Introduction
- 11.3.2. Key Region-Specific Dynamics
- 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.3.7.1. Germany
  - 11.3.7.2. UK
  - 11.3.7.3. France
  - 11.3.7.4. Italy
  - 11.3.7.5. Spain
  - 11.3.7.6. Rest of Europe
11.4. South America
- 11.4.1. Introduction
- 11.4.2. Key Region-Specific Dynamics
- 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.4.7.1. Brazil
  - 11.4.7.2. Argentina
  - 11.4.7.3. Rest of South America
11.5. Asia-Pacific
- 11.5.1. Introduction
- 11.5.2. Key Region-Specific Dynamics
- 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.5.7.1. China
  - 11.5.7.2. India
  - 11.5.7.3. Japan
  - 11.5.7.4. Australia
  - 11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
- 11.6.1. Introduction
- 11.6.2. Key Region-Specific Dynamics
- 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis

13. Company Profiles

13.1. Amazon Web Services Inc.*
- 13.1.1. Company Overview
- 13.1.2. Product Portfolio and Description
- 13.1.3. Financial Overview
- 13.1.4. Key Developments
13.2. Affectiva Inc.
13.3. Nuance Communications Inc.
13.4. Nemesysco Ltd.
13.5. Eyesight Technologies Ltd.
13.6. Element Human Ltd.
13.7. Emotibot Technologies Limited
13.8. Kairos AR, Inc.
13.9. Realeyes Data Services Ltd.
13.10. AUDEERING GmbH

LIST NOT EXHAUSTIVE